Advances in Technology Innovation <p style="margin: 0cm 0cm 0pt;"><strong><em>Advances in Technology Innovation</em></strong> (AITI), ISSN 2518-2994 (Online), ISSN 2415-0436 (Print), is an international, multidiscipline, peer-reviewed scholarly journal. The official abbreviated title is <em><strong>Adv. technol. innov.</strong></em> It is dedicated to providing a platform for fast communication between the newest research works on the innovations of Technology &amp; Engineering. A paper will be online shortly once it is accepted and typeset. Currently, there is no publication charge, including article processing and submission charges. AITI is an open access journal which means that all contents are freely available without charge to the user or his/her institution.</p> <p><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;">AITI is indexed by:</span></p> <p><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;"><img style="width: 136px; height: 26px;" src="" alt="" width="171" height="53">&nbsp; </span><img src="/public/site/images/ijeti/DOAJ4.png" alt=""> &nbsp;&nbsp; <img src="/public/site/images/ijeti/google5.png" alt=""> &nbsp; <img src="" alt="">&nbsp; <img src="/public/site/images/allen/ProQuest-41.png"> <img src="/public/site/images/ijeti/CAB_ABSTRACTS4.png" alt="">&nbsp;&nbsp;<img src="/public/site/images/ijeti/Resarch_Bible5.png" alt="">&nbsp;&nbsp;<img src="/public/site/images/ijeti/WorldCat5.png" alt="">&nbsp;&nbsp;<img src="/public/site/images/allen/academia-12.png"> &nbsp;<img src="/public/site/images/ijeti/TOCs5.jpg" alt=""> &nbsp; <img src="/public/site/images/allen/Publons-22.5_1.png"> &nbsp;&nbsp;<img src="/public/site/images/allen/crossref3.png" width="92" height="42"></p> <p style="margin: 0cm 0cm 0pt;"><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;">&nbsp;Under evaluation of SCI, EI(Compendex), INSPEC, etc.</span></p> <p style="margin: 0cm 0cm 0pt;">&nbsp;</p> en-US <hr style="font: 12px/normal Verdana, Arial, Helvetica, sans-serif; color: rgb(0, 0, 0); text-transform: none; text-indent: 0px; letter-spacing: normal; word-spacing: 0px; white-space: normal; cursor: default; widows: 1; font-size-adjust: none; font-stretch: normal; -webkit-text-stroke-width: 0px;"> <p style="line-height: 150%;"><span style="font-family: Times New Roman;">Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. is accepted for publication. Authors can retain copyright of their article with no restrictions.</span></p> <p style="line-height: 150%;">&nbsp;</p> <p style="line-height: 150%;"><img alt="" src="data:image/png;base64,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"></p> <p style="line-height: 150%;"><span style="font-family: Times New Roman, Times, serif;">Since Jan. 01, 2019, AITI will publish new articles with Creative Commons Attribution Non-Commercial License, under <a href="">The Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License</a>.<br>The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.</span></p> <p style="line-height: 150%;"><span style="font-family: Times New Roman;">&nbsp;</span></p> (The editorial office) (Ms. Lin) Wed, 28 Dec 2022 14:05:09 +0800 OJS 60 An Image-Based Rice Weighing Estimation Approach on Clock Type Weighing Scale Using Deep Learning and Geometric Transformations <p>AI impacts surrounding human life, such as the economy, health, education, and agricultural production; however, the crop prices in the harvest season are still on manual calculation, which causes doubts about accuracy. In this study, an image-based approach is proposed to help farmers calculate rice prices more accurately. YOLOv5 is used to detect and extract the scales in the images taken from the harvesting of rice crops. Then, various image processing techniques, such as brightness balance, background removal, etc., are compiled to determine the needle position and number on the extracted scale. Lastly, geometric transformations are proposed to calculate the weight. A real dataset of 709 images is used for the experiment. The proposed method achieves good results in terms of mAP@0.5 at 0.995, mAP@[0.5:0.95] at 0.830 for scale detection, and MAE at 3.7 for weight calculation.</p> An Cong Tran, Thanh Trinh Thi Kim, Hai Thanh Nguyen Copyright (c) 2023 An Cong Tran, Thanh Trinh Thi Kim, Hai Thanh Nguyen Fri, 03 Feb 2023 09:38:39 +0800 Short-Term Rainfall Prediction Using Supervised Machine Learning <p>Floods and rain significantly impact the economy of many agricultural countries in the world. Early prediction of rain and floods can dramatically help prevent natural disaster damage. This paper presents a machine learning and data-driven method that can accurately predict short-term rainfall. Various machine learning classification algorithms have been implemented on an Australian weather dataset to train and develop an accurate and reliable model. To choose the best suitable prediction model, diverse machine learning algorithms have been applied for classification as well. Eventually, the performance of the models has been compared based on standard performance measurement metrics. The finding shows that the hist gradient boosting classifier has given the highest accuracy of 91%, with a good F1 value and receiver operating characteristic, the area under the curve score.</p> Nusrat Jahan Prottasha, Anik Tahabilder, Md Kowsher, Md Shanon Mia, Khadiza Tul Kobra Copyright (c) 2023 Nusrat Jahan Prottasha, Anik Tahabilder, Md Kowsher, Md Shanon Mia, Khadiza Tul Kobra Thu, 02 Feb 2023 19:24:39 +0800 Online MEMS-Based Specific Gravity Measurement for Lead-Acid Batteries <p>Traditional methods for measuring the specific gravity (SG) of lead-acid batteries are offline, time-consuming, unsafe, and complicated. This study proposes an online method for the SG measurement to estimate the state-of-charge (SoC) of lead-acid batteries. This proposed method is based on an air purge system integrating with a micro electro mechanical system sensor. Through the proposed strategy, the SoC measurement achieves up to ±1% accuracy. The technique has an SG accuracy of ±0.002% which is better than the glass hydrometer accuracy of ±0.005% in the battery charge reading. The experimental results show that the high accuracy and precise measurements of SG and SoC can be conducted by using the proposed method.</p> Yashwant Gulab Adhav, Dayaram Nimba Sonawane, Chetankumar Yashawant Patil Copyright (c) 2023 Yashwant Gulab Adhav, Dayaram Nimba Sonawane, Chetankumar Yashawant Patil Fri, 06 Jan 2023 00:00:00 +0800 Maritime Computing Transportation, Environment, and Development: Trends of Data Visualization and Computational Methodologies <p>This research aims to characterize the field of maritime computing (MC) transportation, environment, and development. It is the first report to discover how MC domain configurations support management technologies. An aspect of this research is the creation of drivers of ocean-based businesses. Systematic search and meta-analysis are employed to classify and define the MC domain. MC developments were first identified in the 1990s, representing maritime development for designing sailboats, submarines, and ship hydrodynamics. The maritime environment is simulated to predict emission reductions, coastal waste particles, renewable energy, and engineer robots to observe the ocean ecosystem. Maritime transportation focuses on optimizing ship speed, maneuvering ships, and using liquefied natural gas and submarine pipelines. Data trends with machine learning can be obtained by collecting a big data of similar computational results for implementing artificial intelligence strategies. Research findings show that modeling is an essential skill set in the 21st century.</p> Thanapong Chaichana Copyright (c) 2023 Thanapong Chaichana Sun, 01 Jan 2023 00:00:00 +0800 On the Estimation of the Mission Performance Index of Unmanned Surface Vehicles Based on the Mission Coverage Area <p>For mission planning and replanning of multiple unmanned surface vehicles (USVs), it is important to estimate each USV’s mission performance in terms of sea surveillance (e.g., illegal ship control). In this study, a mission performance index (MPI) is proposed based on the mission coverage area for estimating the USVs’ mission performance of illegal ship control. The penalty value is considered in the MPI calculation procedure owing to the track-off of the USV. In addition, the USV simulation is conducted under illegal ship control, and the MPI is calculated based on changing the mission coverage area. The results show that the MPI increases with the path width of the mission coverage area.</p> Jae-Yong Lee, Nam-Sun Son Copyright (c) 2023 Jae-Yong Lee, Nam-Sun Son Sun, 01 Jan 2023 00:00:00 +0800 Calculation of Temperature-Dependent Thermal Expansion Coefficient of Metal Crystals Based on Anharmonic Correlated Debye Model <p>This study aims to calculate the anharmonic thermal expansion (TE) coefficient of metal crystals in the temperature dependence. The calculation model is derived from the anharmonic correlated Debye (ACD) model that is developed using the many-body perturbation approach and correlated Debye model based on the anharmonic effective potential. This potential has taken into account the influence on the absorbing and backscattering atoms of all their nearest neighbors in the crystal lattice. The numerical results for the crystalline zinc (Zn) and crystalline copper (Cu) are in agreement with those obtained by the other theoretical model and experiments at several temperatures. The analytical results show that the ACD model is useful and efficient in analyzing the TE of coefficient of metal crystals.</p> Tong Sy Tien, Nguyen Thi Minh Thuy, Vu Thi Kim Lien, Nguyen Thi Ngoc Anh, Do Ngọc Bich, Le Quang Thanh Copyright (c) 2023 Tong Sy Tien, Nguyen Thi Minh Thuy, Vu Thi Kim Lien, Nguyen Thi Ngoc Anh, Do Ngọc Bich, Le Quang Thanh Sun, 01 Jan 2023 00:00:00 +0800